Understanding Consumers’ Perceptions toward Grocery Stores in Bakersfield

Reference

Xu 2021. Brand/Product Positioning & Perceptual Mapping. Rpubs.com/utjimmyx/brand_positioning

Think-Pair-Share

The final perceptual maps (see Figures 3 and 4) are designed to explain the impact of a college degree on people’s perception of grocery stores.

• Figure 1 & 2 represent consumers who have a college degree.
• Figure 3 & 4 represent consumers who do not have a college degree.

Please try to: - Interpret any differences between these two groups of consumers’ perceptions toward grocery stores in Bakersfield. - Suggest any other variables that might be included. - Suggest what a particular grocery store manager should do to make data-informed marketing strategies. - Suggest any changes you would like to make to your own survey questionnaire. How?

Objectives

After this workshop/exercise, you will have a better understanding of how to develop a perceptual mapping for brand/product positioning purposes using either survey data or qualitative data. In addition, you will also learn how to design basic perceptual mapping questions for survey studies.

Tips

• Upload both datasets (Bakersfield_Grocery1.csv & Bakersfield_Grocery2.csv) to R Studio Cloud.
• You only need to run the R Markdown syntax once.

Data Generation Procedures

The first step is to collect your data. In this example, we collected Qualtrics survey data for local groceries in Bakersfield, CA using a focus group. You may evaluate attributes based on your group members’ perceptions of your project client’s business and the competition.

If you would like to follow this tutorial, use at least three attributes and follow the same data format (columns = attributes; first row = brands or products). Please open the sample data to see how it is structured.

Data Pre-processing

We first calculate consumers’ average ratings for each attribute, then split the original survey dataset into two groups according to the education variable (1 and 0):

• Bakersfield_Grocery1.csv — consumers who have at least a college degree.
• Bakersfield_Grocery2.csv — consumers who do not have a college degree.

Interpreting the Results — What Does a Perceptual Map Tell You?

The final output shows how different grocery brands are clustered in four quadrants, reflecting how they are perceived across several service attributes (e.g., Yelp Reviews, Convenience, Price/Value).

Setup — Install and Load Required Packages

# Install and load required packages (only needs to run once)
if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")
if (!requireNamespace("ggbiplot", quietly = TRUE)) remotes::install_github("vqv/ggbiplot")

library(ggbiplot)

# Group labels (3 stores x 3 groups)
site.groups <- c(rep("a", 3), rep("b", 3), rep("c", 3))

Helper Function — Build Biplot

# Reusable function to generate both a plain and styled biplot
build_biplot <- function(filepath, site.groups) {
  data     <- read.csv(filepath, header = TRUE, row.names = 1)
  data.pca <- prcomp(data, scale. = TRUE)
  
  # Plain biplot
  print(ggbiplot(data.pca, labels = rownames(data)))
  
  # Styled biplot with ellipses
  g <- ggbiplot(data.pca,
                obs.scale = 1, var.scale = 1,
                groups     = site.groups,
                ellipse    = TRUE, circle = TRUE,
                labels     = rownames(data))
  g <- g + scale_color_discrete(name = "")
  g <- g + theme(legend.direction = "horizontal",
                 legend.position  = "top")
  print(g)
}

Figures 1 & 2 — Perceptions of Consumers With a College Degree

build_biplot("Bakersfield_Grocery1.csv", site.groups)

Figures 3 & 4 — Perceptions of Consumers Without a College Degree

build_biplot("Bakersfield_Grocery2.csv", site.groups)

Disclaimer

The sample size used for this survey exercise is small. Please do not generalize the results to the entire population. For generalization purposes, a survey of at least 30 customers is typically required for most statistical analyses.

References

Nenadic, O., & Greenacre, M. (2007). Correspondence analysis in R, with two- and three-dimensional graphics: the ca package. Journal of Statistical Software, 20(3). https://goedoc.uni-goettingen.de/bitstream/handle/1/5892/Nenadic.pdf?sequence=1

Sensographics and Mapping Consumer Perceptions Using PCA and FactoMineR. https://www.r-bloggers.com/2017/09/sensographics-and-mapping-consumer-perceptions-using-pca-and-factominer/

The Unavoidable Instability of Brand Image. https://www.r-bloggers.com/2014/06/the-unavoidable-instability-of-brand-image/

The R Project for Statistical Computing. https://www.r-project.org/

R for Data Science — Hadley Wickham. https://r4ds.had.co.nz/

R Markdown. https://rmarkdown.rstudio.com/

R Markdown: The Definitive Guide. https://bookdown.org/yihui/rmarkdown/